Abstract
The unbundling of the electricity sector in several activities, some of them provided in a regulated way and some others under competition, poses a number of challenging problems namely because in several areas there are conflicting objectives associated to different stakeholders. These different views and objectives paved the way to the development of new multiobjective tools able to represent this new paradigm. In this scope, this paper presents a multiobjective (MO) formulation for the Transmission Expansion Planning (TEP) problem using a new solution approach that combines concepts of evolutionary computation and multi agent population algorithms. The new proposed tool is termed as Multi-Population and Multiobjective Evolutionary Particle Swarm Optimization - MEPSO-II. The TEP problem is handled in a realistic way preserving the holistic view over the entire planning horizon and the true grid behavior because it considers the multi-stage nature of the problem and we use an AC Optimal Power Flow (AC-OPF) model to gain insight on the operation conditions of the network. The multi objective formulation considers the total system cost, on one side, and the Expected Power Not Supplied (EPNS), on the other. The total system cost comprises the investment cost in new equipment and the operation costs while the EPNS takes into account the uncertainties related to the non- ideal behavior of system components using a non-chronological Monte Carlo simulation. Numerical simulations are conducted using the IEEE 24 and the 118 Bus Test Systems in order to compare the proposed MO tool against other algorithms through performance evaluation indices. Although being a higher time-consuming tool, the MEPSO-II enables improving the Pareto-Front and therefore it gives more insight to transmission network planners when compared with other consolidated algorithms described in the literature.

Abstract
Over the last few years, electrical storage and especially battery systems have seen a strong rise in interest. In several countries, as for instance in Germany, lithium-ion batteries are now commonly deployed in end-consumer installations to shift local generation from photovoltaic systems in time. A further application for storage is price arbitrage, which corresponds to an operation strategy benefitting from price differentials. In this work, we describe a Mixed Integer Problem to optimize the storage dispatch considering both the 15- and the 60-min auctions in use in Germany. Furthermore, in addition to the calendric lifetime, the limitation to a certain number of cycles is considered in the evaluation. Last, it was conducted a sensitivity analysis to identify the price volatility level that is required to generate a profit from arbitrage operations. Therefore, a market price process with adjustable parameters has been implemented.

Abstract
Electric vehicles will certainly play an important and increasing role in the transport sector over the next years. As their number grows, they will affect the behavior of the electricity demand seen not only by distribution but also by transmission networks and so changes will also occur in the operation and expansion planning of the power systems. In this sense, this paper addresses the impact of large fleets of Plug-in-Electric Vehicles (PEVs) in transmission equipment investments. The developed model uses evolutionary particle swarm optimization (EPSO) to handle the planning problem over different scenarios regarding the evolution of PEVs and their impact on the demand. These scenarios consider the PEVs penetration level, the availability of charging and the related charging policies. The paper includes a Case Study based on the IEEE 24 busbar power system model for a 10-year period. The model uses an AC Optimal Power Flow to analyse the operation of the system for different investment paths over the years and the results show that coordinating the charging of PEVs can be an interesting solution to postpone the investments in transmission equipment thus reducing the associated costs.

Abstract
The Brazilian electricity market is characterized by having around 65% of its installed capacity coming from hydropower plants, with multiple agents coexisting in the same hydro cascades. Currently, it also contains certain peculiarities that distinguish it from other markets, such as the Energy Reallocation Mechanism (MRE), a centerpiece of the Brazilian market's design. This paper proposes replacing the MRE with a bid-based short-term market called the virtual reservoir model. To simulate the behavior of the hydros in this new market, an agent-based model is implemented using the reinforcement Q-learning algorithm, simulated annealing and linear programming. In the simulations, we use real data - encompassing more than 98% of the total hydro installed capacity and three years of market data - from the Brazilian power system. The results indicate that the management of (virtual) reservoirs can be the responsibility of each hydro: these can save water according to their own risk perceptions, while maintaining current efficiency and security levels. The results also suggest that the final monthly short-term market prices can substantially decrease in comparison with the current prices.

Abstract
A flexible control of the distribution system is an efficient strategy to enhance the grid reliability and quality of service, in this sense, the maneuver devices play an important role to reach flexibility under network faults. In this direction, the present work proposes a new approach to solve the allocation problem of optimal maneuver devices in electric distribution systems (EDS) that considers both permanent and temporary faults. The considered maneuver devices are normally closed switches coupled to the beginning or to the end of distribution branches. The objective is to improve the system reliability with minimal investment cost. The metaheuristic and bio-inspired technique known as artificial immune system (AIS) is applied to handle the discrete feature of the switch allocation problem. The index considered to evaluate the reliability is the system expected outage cost to customers due to supply outages (ECOST). The paper includes a case study with four different simulations using the well-know RBTS Bus 4 test system The obtained results were compared to the literature ones and proved that the proposed approach can lead to promising solutions that establish a suitable trade-off between the reliability and the utility costs. © 2018 Power Systems Computation Conference.

Abstract
This paper results from the research conducted by the first author during the preparation of his MSc Thesis. This research aimed at investigating the impact on the market prices of the Iberian Electricity Market, MIBEL, due to increases of the feed-in generation, as such an increase is expected to occur in the next few years, namely for PV systems. This research was conducted using real market data publicly available in the web site of the Iberian Market Operator for 2016. To estimate this impact, for each trading hour of 2016 we considered new segments at price 0,0 (sic)/MWh to translate the priority given to this type of generation. These segments representing the new feed-in generation were then used together with the selling bids submitted by market agents to build the new aggregated selling curve. The new market price was finally obtained as the intersection of the new selling curve with the original buying curve, that was assumed unchanged. The global result indicates that if the feed-in generation increases by 25% regarding the values of 2016, then the average annual market price decreases by 6,57 % regarding the original value of 39,42 (sic)/MWh.